Determinants of Utilization of Maternal Healthcare Services in Ethiopia
Bibliographic record
Abstract
Utilizing maternal healthcare services, such as antenatal care, professionals’ assistance during delivery and postnatal care contributes significant role in reduction of maternal and child mortality. However, there are many factors both at individual and community level that affect utilization of these required services. To determine the levels of effects of socio-economic and demographic factors on uses of Maternal Healthcare services 7764 women who had given birth at least one times have taken from the 2011 Ethiopian DHS. The results showed that the rate of safe motherhood practices among reproductive age group of women in Ethiopia were too low. About 51 percent of them did not use any health care services during pregnancy, childbirth, and post-delivery periods. As WHO recommend only 6.9 percent of women were attending ANC at least four times, assisted by health professional during delivery and received PNC. The result of logistic regression showed that antenatal care, skilled delivery and postnatal care utilizations were commonly influenced by place of residence, wealth status, women’s and husband’s education and parity. Whereas, mother’s working status and husband’s education were found to be uniquely influence the uses of ANC and PNC services, respectively. In addition, both religious affiliation and age of women were also prominent predictors on utilization of ANC and uses of skilled assistance during delivery. Based on these significant factors, it is important to design and promote uses of maternal healthcare services in order to minimize the risk of maternal and child mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".